Providing answers to questions having both rankable and probabilistic components

ABSTRACT

Providing answers to questions. A LAT, a rankable criterion, and a probabilistic criterion are identified in a received query. A set of candidate answers to the question that are instances of the LAT identified in a first data source is generated. Based on the rankable criterion, the candidate answers are ranked. Each candidate answer is assigned a likelihood that the candidate answer satisfies the probabilistic criterion, based on a statistic of occurrences of terms related to the candidate answer and the probabilistic criterion in text passages from a second data source. One or more candidate answers are selected based on the rank and the likelihood of the candidate answers. In another aspect of the invention, a Boolean criterion is also identified in the query and the set of candidate answers is reduced by applying the Boolean criterion.

BACKGROUND

The present invention relates generally to the field of questionanswering technology, and more particularly to reconciling simultaneousranking criteria in generating answers.

Question Answering (QA) is a computer science discipline within thefields of information retrieval and natural language processing (NLP)which is concerned with building systems that automatically answerquestions posed in a natural language. A QA implementation, usually acomputer program, may construct answers by querying a structureddatabase, or table, of knowledge or information, such as a knowledgebase. More commonly, QA systems may generate answers from anunstructured collection of natural language documents, or text corpus.

Unstructured data refers to information that is not organized accordingto a data model, which specifies how the data items relate to oneanother. Unstructured information is typically text heavy, but maycontain data such as dates, numbers, and facts as well. Unstructureddata may be indexed. For example, the occurrences of each word in a textdocument of unstructured data may be recorded in an index structure.Structured data is data that is organized according to a data model orschema. Generally, the term structured data is applied to relationaldatabases and unstructured data applies to everything else.

Superlative/ordinal, or rankable, QA answers questions that include anordinal, giving a rank or position in a sequence, such as “first”,“second”, or “last”; or a superlative, indicating being of extremedegree, such as “largest”, “smallest”, or “fastest”; or a combination ofsuperlative and ordinal, such as “second largest”. Examples of rankablequestions include “Who was the first/10th/most recent president?”, “Whatis the largest state?”, and “What is the 3rd tallest mountain?”. Thistype of QA typically requires lookup in a structured database orknowledge base.

Rankable criteria for structured data are often paired with Booleanfilters, which may reduce the set of possible answers. For example, inthe question “Who was the last Republican president?”, the word“Republican” acts as a Boolean filter on the set of presidents. The termBoolean implies that a given criterion is either entirely true orentirely false for the objects considered.

Non-rankable QA addresses questions that are not posed as rankablequestions. Examples include “Which president had a handlebar mustache?”or “What country exports coffee and is home to elephants?”. Sincestructured data may not exist to answer this type of question, this typeof QA typically requires identifying passages in an unstructured textcorpus, for example, using keywords in the question, in order toestimate the probability that a candidate answer is correct. QA systemsthat operate in this way are referred to as probabilistic QA systems.

SUMMARY

Embodiments of the present invention disclose a computer-implementedmethod, computer program product, and system for providing answers toquestions. A question is received. A lexical answer type (LAT), arankable criterion, and a probabilistic criterion in the question areidentified. A set of candidate answers to the question that areinstances of the LAT identified in a first data source is generated.Based on the rankable criterion, the candidate answers are ranked. Toeach candidate answer is assigned a likelihood that the candidate answersatisfies the probabilistic criterion, based on a statistic ofoccurrences of terms related to the candidate answer and to theprobabilistic criterion in text passages from a second data source. Oneor more of the candidate answers are selected based on the rank and thelikelihood of the candidate answers. The selected candidate answers aretransmitted.

In another aspect of the invention a LAT, a rankable criterion, aprobabilistic criterion, and a Boolean criterion are identified in thequestion. The set of candidate answers is reduced by applying theBoolean criterion.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a functional block diagram of a rankable-probabilisticquestion answering (RPQA) system, in accordance with an embodiment ofthe present invention.

FIG. 2 is a flowchart depicting operational steps of an RPQA program, inaccordance with an embodiment of the present invention.

FIG. 3 is a functional block diagram of a data processing environment,in accordance with an embodiment of the present invention.

FIG. 4 is a functional block diagram of a cloud computing node accordingto an embodiment of the present invention.

FIG. 5 depicts a cloud computing environment according to an embodimentof the present invention.

FIG. 6 depicts abstraction model layers according to an embodiment ofthe present invention.

DETAILED DESCRIPTION

Embodiments of the present invention are directed torankable-probabilistic QA systems (RPQA) that may receive queries havingboth a superlative/ordinal, or rankable, component, and a probabilisticcomponent, and which access structured and unstructured data sources togenerate a ranked list of candidate answers. For example, in thequestion “Who was the last president to serve in the military?”, “last”defines a rankable component and “serve in the military” defines aprobabilistic component. The final ranking reconciles the rankablecriteria and the probabilistic criteria.

The terms “question” and “query,” and their extensions, may be usedinterchangeably and refer to the same concept, namely a request forinformation received by the RPQA system. Such requests are typically inthe form of an interrogative sentence in a natural language, but theymay also be in other forms, for example as a declarative sentenceproviding a description of an entity of interest, where the request forthe identification of the entity can be determined from the sentence. Aquery may also be generated via a graphical user interface (GUI), forexample as part of an expert system. In this case, a user may constructa query by manipulating graphical control elements in the GUI.

Structured data is defined as information that is explicitly representedin the structure or format of the data, for example a database table,and whose intended meaning is unambiguous. Structured data is generallystored in structured data sources, such as relational databases andcertain hierarchical knowledge bases. Semi-structured data is a form ofstructured data that does not conform to the formal structure of datamodels associated with relational databases or other forms of datatables, but nonetheless contains tags or other markers to separatesemantic elements and enforce hierarchies of records and fields withinthe data. Examples of semi-structured data include XML documents andemail. Unstructured data is defined as information whose intendedmeaning can be implied by its content, for example a natural languagedocument. Unstructured data is stored in unstructured data sources,which may include text corpora and other datastores of natural languagedocuments. Structured data may also be found in unstructured datasources, for example as a table embedded in a Web page.

FIG. 1 is a functional block diagram of an RPQA system 100, inaccordance with an embodiment of the present invention. RPQA system 100includes server 110 and client computing devices 126, all interconnectedover a network 130.

Network 130 can be, for example, a local area network (LAN), a wide areanetwork (WAN) such as the Internet, or a combination of the two, and caninclude wired, wireless, or fiber optic connections. In general, network130 can be any combination of connections and protocols that willsupport communications between server 110 and client computing devices126, in accordance with embodiments of the invention.

In an exemplary embodiment, client computing devices 126 represent asource of queries that are received by server 110. Client computingdevices 126 may include graphical user interfaces 128, by which a usermay enter a query and receive an answer. In various embodiments,graphical user interfaces 128 may be, for example, web browsers thatreceive web pages transmitted by server 110, or dedicated applicationsthat interface with RPQA program 112 on server 110. In otherembodiments, client computing devices 126 may represent other sources ofqueries, for example, expert systems, speech recognition systems coupledto voice entry devices, other servers, etc.

In various embodiments of the invention, a client computing device 126may be, for example, a laptop computer, tablet computer, netbookcomputer, personal computer (PC), a desktop computer, a personal digitalassistant (PDA), or a smart phone. In general, a client computing device126 may be any programmable electronic device capable of communicatingwith server 110 via network 130, and of supporting functionality asrequired by one or more embodiments of the invention. A client computingdevice 126 may include internal and external hardware components asdepicted and described in further detail below with reference to FIG. 3.

Server 110 represents the computing environment or platform that hostsRPQA program 112. In various embodiments, server 110 may be a laptopcomputer, netbook computer, personal computer (PC), a desktop computer,or any programmable electronic device capable of hosting RPQA program112, described below, and communicating with client computing devices126 via network 130, in accordance with embodiments of the invention.Server 110 may include internal and external hardware components, asdepicted and described in further detail below with reference to FIG. 3.In various embodiments, client computing device 126 and server 110 maybe one and the same. In other embodiments, server 110 may represent acloud computing environment, as described in relation to FIGS. 4, 5, and6, below.

In an embodiment of the invention, server 110 includes RPQA program 112.RPQA program 112 may further include query analysis module 114,candidate answer generation module 116, probabilistic answer scoringmodule 118, hybrid answer ranking module 120, structured data sources122, and unstructured data sources 124.

In an exemplary embodiment of the invention, query analysis module 114operates generally to receive an input query, for example from a clientcomputing device 126, and identify in the query a lexical answer type, aset of Boolean criteria, CB, a set of rankable criteria, CR, and a setof probabilistic criteria, CP.

In an embodiment of the invention, query analysis module 114 may locateand classify elements in the query into predefined categories, such asnames of persons, organizations, locations, time expressions,quantities, plants, animals, or events by, for example, named entityrecognition (NER) programs. Query analysis module 114 may then determinethe grammatical structure of the query, for example, which groups ofwords go together as phrases and which words are the subject or objectof a verb by, for example, natural language parsing programs. This mayinclude tagging the words of the query with parts of speech, such asnouns, verbs, adjectives, adverbs, pronouns, conjunctions, prepositions,interjections, and articles. This may be accomplished, for example,using the Stanford Parser, version 3.5.1, available from The StanfordNatural Language Processing Group at Stanford University, or otherproprietary and/or open source parsers.

A lexical answer type (LAT) is a word or noun phrase in a question thatspecifies the type of the answer. For example, in the question “Who wasthe last president to have served in the military?” the LAT is“president.” A question may contain multiple LATs. Identifying the LATsin the query may be accomplished by keyword extraction, in conjunctionwith classifying the elements in the query into predefined categories,for example, via an NER program, determining the grammatical structureof the query, and tagging the words in the query with parts of speech.For example, a defined list of LATs may include one or more keywordsassociated with each LAT. If a keyword is found in the query, forexample, an interrogative keyword such as “Who”, “Where”, or “How many”,this may indicate that the LAT should be “Person”, “Location”, or“Number”, respectively. The keyword word “When” may indicate that theLAT may be of type “Date”.

CR in the query may be identified via comparison in a knowledge basewith the extracted keywords and the identified parts of speech. Forexample, except for a handful of exceptions such as “worst”,superlatives are usually adjectives preceded by “the” and ending in“est”, or preceded by “the most”. Superlatives also normally come beforeany other adjectives modifying a noun. Except for a few exceptions, suchas “primary” and “penultimate”, ordinals usually derive from numbers andcarry one of the suffixes “nd”, “rd”, “st”, or “th”. Examples includeseventh, twenty-third, 15th, and 41st.

CB candidates may be words or phrases in a query which modify LATs, suchas adjectives or prepositional phrases. CB candidates may be identified,for example, by a natural language parser. In an embodiment, anidentified CB candidate may be in CB if, for example, the modifyingphrase appears as a column heading of a table in structured data sources122, listing instances of the LAT, with column entries “yes” or “no”; oralternatively, the adjective or adjective phrase is a valid column entryin such a table with an appropriate categorical column heading. Forexample, for the query “Who was the last Republican president to servein the military?”, the term “Republican” may be identified as in CB if atable of presidents may be found in structured data sources 122, inwhich “Republican” appears, or could appear, as an entry in a columnwith heading “Party Affiliation”.

CP candidates may be words or phrases in the query which modify LATs,such as adjectives or prepositional phrases. CP candidates may beidentified, for example, by a natural language parser. An identified CPcandidate may be in CP if it is not identified as being in CR or CB, asdescribed above.

For example, for the query “Who was the last Republican president toserve in the military?”, the LAT may be “president”, CB may be“Republican”, CR may be the superlative “last”, and CP may be “served inthe military”. In other exemplary queries, CB may not be present.

In an exemplary embodiment of the invention, candidate answer generationmodule 116 operates generally to receive the parsed query from queryanalysis module 114, and access and analyze documents in structured datasources 122, based on information associated with the parsed query, tocreate a list of candidate answers to a query received by RPQA program112. The candidate answers, which may be instances of a LAT identifiedby query analysis module 114, may be filtered via CB identified by queryanalysis module 114, and ranked according to CR identified by queryanalysis module 114.

Candidate answer generation module 116 operates generally to identifyinitial candidate answers to the query, based on information instructured data sources 122. Candidate answer generation module 116accesses and analyzes documents in structured data sources 122,described in more detail below, to determine whether the LAT identifiedby query analysis module 114 forms a closed class with known instances,identifies a set of known instances of the LAT, and creates a list ofcandidate answers, filtered via CB, and ranked according to CR. In somecases, certain candidate answers in the list may be equally ranked.Typically, LATs that form a closed class with known instances, which maybe filtered via CB and ranked according to CR, may be found instructured data sources 122 in the form of a table or fixed list,ordered according to CR and annotated to indicate compliance with CB.For example, for the query “Who was the last Republican president toserve in the military?”, the initial list of candidate answers may bederived from a table of presidents, for example, found in structureddata sources 122, ordered by election date, and tagged with partyaffiliation. Since CR is the superlative “last”, the initial list ofcandidate answers may be ordered from last to first, so that the “top”answer is the first one. In embodiments in which multiple LATs may beidentified by query analysis module 114, candidate answer generationmodule 116 may identify candidate answers that are jointly instances ofall the identified LATs.

In some embodiments, candidate answer generation module 116 may extendan identified LAT by combining it with one or more modifying phrases,which are identified as Boolean or probabilistic criteria. If, forexample, a LAT, together with a modifying phrase, appears as a columnheading of a table in structured data sources 122, listing instances ofthe extended LAT, then the extended LAT may be used instead of theidentified LAT. If, for example, for the query “Who was the lastRepublican president to serve in the military?”, a table of Republicanpresidents may be found in structured data sources 122, then “Republicanpresident” may serve as the LAT and there may be no Boolean criterion.For the query “Who was the first woman in space who owned a dog?”, forexample, “woman” may be identified as a LAT, while “in space” and “owneda dog” may be identified as CP. However, “woman” does not form a closedclass with known instances, while “woman in space” does, hence a searchin structured data sources 122 may identify a table of women in space.In this case, “woman in space” may serve as the LAT and CP may be “owneda dog”.

Structured data sources 122 represents a store of structured orsemi-structured data that may be processed in the context of an RPQAsystem, in accordance with an embodiment of the invention. Structureddata sources 122 may reside, for example, on computer readable storagemedia 308 (FIG. 3), and/or on cloud computing node storage system 34(FIG. 4). The structured data may include documents, for example, textdocuments, such as documents generated by Microsoft Word®, OpenOffice®Writer®, or other proprietary or open source word processing systems.The structured data may also include records, or fields within records,created by special purpose application systems. For example, in anexemplary embodiment, a structured data item may be one or more medicalcase note entries in an electronic health record (EHR) or electronicmedical record (EMR) created by a medical professional during a patientexamination using a proprietary or open source EHR/EMR system. In otherembodiments, the structured data may include data in other forms, suchas a video, a picture, a file written in HTML, or other formats such astext, XML, or PDF.

In an exemplary embodiment of the invention, probabilistic answerscoring module 118 receives the ranked list of candidate answersgenerated by the candidate answer generation module 116, and utilizesdocuments from unstructured data sources 124 to assign a likelihood toeach element in the ranked list of candidate answers generated by thecandidate answer generation module 116. The likelihood represents theprobability that the candidate answer satisfies CP, for example, astatistic of occurrences of terms related to the candidate answer and toCP in text passages from unstructured data sources 124. For example, forthe query “Who was the last Republican president to serve in themilitary?”, the likelihood might represent the probability that apresident had served in the military, based on an analysis of textpassages retrieved from the unstructured data sources 124. In computinga likelihood, probabilistic answer module 118 may consider the degree ofmatch between the retrieved passages' predicate-argument structure andCP, passage source reliability, geospatial location, temporalrelationships, taxonomic classification, lexical and semantic relationsthe candidate is known to participate in, the candidate's correlationwith the terms of CP, its popularity (or obscurity), its aliases, and soon. For example, in a typical embodiment, probabilistic answer scoringmodule 118 may combine the candidate answer with the probabilisticcomponent of the query in searching the unstructured data sources 124 toretrieve short passages, or snippets, that contain the candidate answerin the context of the terms of CP, use an NLP topic modeling techniquesuch as Latent Dirichlet Allocation to learn a set of topics, or wordsthat tend to appear together, from snippets, as well as probabilitiesthat a snippet belongs to each topic, and use these probabilities tocompute the probability of CP given the candidate answer, for example,by using Bayes' rule.

In other embodiments of the invention, probabilistic answer scoringmodule 118 may identify, based on a threshold value or filter, a subsetof the ranked candidate answers generated by candidate answer generationmodule 116, and may assign a likelihood only to elements in the subsetof ranked of candidate answers.

Unstructured data sources 124 represents a store of unstructured datathat may be processed in the context of the RPQA system, typically textcorpora. Unstructured data sources 124 may reside, for example, oncomputer readable storage media 308 (FIG. 3), and/or on cloud computingnode storage system 34 (FIG. 4). The unstructured data may includedocuments, for example, plain text documents, scanned documents, ADOBE®Portable Document Files (PDFs), and Microsoft® Word documents, as wellas Web content such as online news and blogs.

In alternative embodiments, assigning a likelihood to each candidateanswer according to CP may occur first, followed by ranking thecandidate answers according to CR, without affecting the outcome. Forexample, for the query “Who was the last Republican president to servein the military?”, candidate answer generation module 116 may identifyknown instances of the LAT, “president”, filter them via CB,“Republican”, and assign a likelihood to each instance according to CP,“served in the military”, as described above. The candidate answers maythen be ranked according to CR, “last”, as described above. While CB istypically applied as early as possible to reduce the number of candidateanswers, it may also be applied at a later stage.

Hybrid answer ranking module 120 operates to reconcile CR and CP toproduce a hybrid score, according to which the candidate answersidentified by candidate answer generation module 116 may be ranked. Forexample, for the query “Who was the last Republican president to servein the military?”, the candidate answer with the highest hybrid scoremay represent the Republican president most likely to be the last one tohave served in the military. Hybrid answer ranking module 120 maycompute a hybrid score, for example, as illustrated in Table 1.

For certain queries it may be necessary for hybrid answer ranking module120 to compute hybrid scores multiple times for the candidate answersidentified by candidate answer generation module 116. For example, forthe query “Who was the second president to have a mustache?”, with CRthe ordinal “second” and CP “to have a mustache”, hybrid answer rankingmodule 120 may compute a hybrid score to identify a candidate answerlikely to be the first president to satisfy CP, eliminate the identifiedfirst president and all previous presidents from the list of candidatesanswers, and then compute hybrid scores for all candidates in thereduced list, in order to identify a candidate answer likely to be thesecond president to satisfy CP.

Table 1 is an example program in pseudo-code, that implements a combinedranking procedure, in accordance to an exemplary embodiment of theinvention. The output of line 1 is a list of candidate answers {L₁, . .. , L_(n)}, filtered relative to CB and ranked according to CR, with L₁the top ranked candidate, L₂ the next lower ranked candidate, and so on.For each candidate L_(i), a function P provides a probability p_(i) thatCP is satisfied (line 4). If it could be discerned that L₁ satisfies CP,it would be the answer sought. If it does not, but L₂ does, then L₂would be the sought after answer; and so on down the list. For example,suppose that the probability that L₁ satisfies CP is p₁. The next entityL₂ is only the “right” answer if L₁ is not. The probability that L₂satisfies CP and L₁ does not satisfy CP is the product p₂(1−p₁).Similarly, the probability that L₃ is the answer is the productp₃(1−p₂)(1−p₁), and likewise for every other entity in the list. Thecomputational loop in lines 3-7 assigns scores S(L_(i)) according tothis schema. Line 6 addresses candidates that are equally rankedrelative to CR.

TABLE 1 SAMPLE PROGRAM IN PSEUDO-CODE FOR IMPLEMENTING HYBRID SORT 1Rank and filter input candidates by CR and CB, producing candidate list{L₁, L₂, L₃ ... L_(n)}. 2 w = 1.0 3 for i = 1 .. n do 4 p = P(L_(i)) 5S(L_(i)) = w * p 6 Repeat previous step for all candidates L_(j)following L_(i) with the same rank according to CR. Advance i to thelast of these. 7 w = w * (1.0 − p) 8 Sort the candidates by S

After the candidate answers generated by candidate answer generationmodule 116 have been ranked by hybrid answer ranking module 120, theranked list may be formatted and returned to the requestor, for example,for display on a GUI 128.

In other embodiments of the invention, probabilistic answer scoringmodule 118 may identify, based on a threshold value or filter, a subsetof the candidate answers generated by candidate answer generation module116, and hybrid answer ranking module 120 may rank and return onlyelements in the identified subset of candidate answers.

FIG. 2 is a flowchart depicting operational steps of RPQA program 112,in accordance with an exemplary embodiment of the invention. RPQAprogram 112 may receive a query (step 210), for example, from a clientcomputing device 126. Query analysis module 114 analyzes the query toidentify a LAT, CB, CR, and CP (step 212). Candidate answer generationmodule 116 determines whether the LAT forms a closed class with knowninstances (decision step 214). If the LAT does not form a closed classwith known instances (decision step 214, “NO” branch), processing ends.If the LAT forms a closed class with known instances (decision step 214,“YES” branch), candidate answer generation module collects the knowninstances (step 216) and stores them in an internal or externaldatastore, for example on computer readable storage media 308. Candidateanswer generation module 116 may reduce the set of known instances byapplying the filter associated with CB (step 218). Candidate answergeneration module 116 determines whether the set of instances, viewed asstructured data, may be ranked according to CR (decision step 220). Ifthe set of instances cannot be ranked according to CR (decision step220, “YES” branch), candidate answer generation module 116 ranks themaccording to CR (step 222). In some cases, certain candidates may beequally ranked according to CR. If the set of instances may not beranked according to CR (decision step 220, “NO” branch), processingends. Probabilistic answer scoring module 118 assigns a probability P,representing a confidence score relative to CP, to each ranked candidateanswer (step 224). Hybrid answer ranking module 120 assigns a hybridscore S, which reconciles CR with CP, to each element in the ranked listof candidate answer (step 226). RPQA program 112 may order the list ofcandidate answers according to the hybrid scores (step 228). In typicalembodiments, only the element in the final list of candidate answerswith highest hybrid score is required; other embodiments may employ theentire list.

For example, if the query received by RPQA program 112 is “Who was thelast Republican president to have served in the military?”, queryanalysis module 114 may determine that the LAT is “president”, CB is“Republican,” CR is election date, and CP is “served in the military”.Candidate answer generation module 116 may collect all known instancesof the LAT “president”, reduce the list of presidents according to CB toRepublican presidents, and rank the list of Republican presidents by CR,their date of election, to produce a ranked list of candidate answers.Probabilistic answer scoring module 118 may assign a likelihoodaccording to CP that each Republican president served in the military.Hybrid answer ranking module 120 may assign a hybrid score, based on thepresident's election date and the likelihood that the president servedin the military, such that the president with the highest hybrid scoreis the president most likely to be the last Republican president to haveserved in the military.

FIG. 3 depicts a block diagram 300 of components of client computingdevices 126 and/or a server 110 of FIG. 1, in accordance with anembodiment of the present invention. It should be appreciated that FIG.3 provides only an illustration of one implementation and does not implyany limitations with regard to the environments in which differentembodiments may be implemented. Many modifications to the depictedenvironment may be made.

Client computing devices 126 and/or a server 110 may include one or moreprocessors 302, one or more computer-readable RAMs 304, one or morecomputer-readable ROMs 306, one or more computer readable storage media308, device drivers 312, read/write drive or interface 314, networkadapter or interface 316, all interconnected over a communicationsfabric 318. Communications fabric 318 may be implemented with anyarchitecture designed for passing data and/or control informationbetween processors (such as microprocessors, communications and networkprocessors, etc.), system memory, peripheral devices, and any otherhardware components within a system.

One or more operating systems 310, and one or more application programs328, for example, RPQA program 112, are stored on one or more of thecomputer readable storage media 308 for execution by one or more of theprocessors 302 via one or more of the respective RAMs 304 (whichtypically include cache memory). In the illustrated embodiment, each ofthe computer readable storage media 308 may be a magnetic disk storagedevice of an internal hard drive, CD-ROM, DVD, memory stick, magnetictape, magnetic disk, optical disk, a semiconductor storage device suchas RAM, ROM, EPROM, flash memory or any other non-transitory,computer-readable, tangible storage device, which can store a computerprogram and digital information.

Client computing devices 126 and/or a server 110 may also include a R/Wdrive or interface 314 to read from and write to one or more portablecomputer readable storage media 326. Application programs 328 on clientcomputing devices 126 and/or a server 110 may be stored on one or moreof the portable computer readable storage media 326, read via therespective R/W drive or interface 314 and loaded into the respectivecomputer readable storage media 308.

Client computing devices 126 and/or a server 110 may also include anetwork adapter or interface 316, such as a TCP/IP adapter card orwireless communication adapter (such as a 4G wireless communicationadapter using OFDMA technology). Application programs 328 on clientcomputing devices 126 and/or a server 110 may be downloaded to thecomputing device from an external computer or external storage devicevia a network (for example, the Internet, a local area network or otherwide area network or wireless network) and network adapter or interface316. From the network adapter or interface 316, the programs may beloaded onto computer readable storage media 308. The network maycomprise copper wires, optical fibers, wireless transmission, routers,firewalls, switches, gateway computers and/or edge servers.

Client computing devices 126 and/or a server 110 may also include adisplay screen 320, a keyboard or keypad 322, and a computer mouse ortouchpad 324. Device drivers 312 interface to display screen 320 forimaging, to keyboard or keypad 322, to computer mouse or touchpad 324,and/or to display screen 320 for pressure sensing of alphanumericcharacter entry and user selections. The device drivers 312, R/W driveor interface 314 and network adapter or interface 316 may comprisehardware and software (stored on computer readable storage media 308and/or ROM 306).

The present invention may be a system, a method, and/or a computerprogram product. The computer program product may include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Smalltalk, C++ or the like, andconventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

The programs described herein are identified based upon the applicationfor which they are implemented in a specific embodiment of theinvention. However, it should be appreciated that any particular programnomenclature herein is used merely for convenience, and thus theinvention should not be limited to use solely in any specificapplication identified and/or implied by such nomenclature.

Based on the foregoing, a computer system, method, and computer programproduct have been disclosed. However, numerous modifications andsubstitutions can be made without deviating from the scope of thepresent invention. Therefore, the present invention has been disclosedby way of example and not limitation.

It is understood in advance that although this disclosure includes adetailed description on cloud computing, implementation of the teachingsrecited herein are not limited to a cloud computing environment. Rather,embodiments of the present invention are capable of being implemented inconjunction with any other type of computing environment now known orlater developed.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g. networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. This cloud model may includeat least five characteristics, at least three service models, and atleast four deployment models.

Characteristics are as follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported providing transparency for both theprovider and consumer of the utilized service.

Service Models are as follows:

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client devices through athin client interface such as a web browser (e.g., web-based e-mail).The consumer does not manage or control the underlying cloudinfrastructure including network, servers, operating systems, storage,or even individual application capabilities, with the possible exceptionof limited user-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting forload-balancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure comprising anetwork of interconnected nodes.

Referring now to FIG. 4, a schematic of an example of a cloud computingnode is shown. Cloud computing node 10 is only one example of a suitablecloud computing node and is not intended to suggest any limitation as tothe scope of use or functionality of embodiments of the inventiondescribed herein. Regardless, cloud computing node 10 is capable ofbeing implemented and/or performing any of the functionality set forthhereinabove.

In cloud computing node 10 there is a computer system/server 12, whichis operational with numerous other general purpose or special purposecomputing system environments or configurations. Examples of well-knowncomputing systems, environments, and/or configurations that may besuitable for use with computer system/server 12 include, but are notlimited to, personal computer systems, server computer systems, thinclients, thick clients, hand-held or laptop devices, multiprocessorsystems, microprocessor-based systems, set top boxes, programmableconsumer electronics, network PCs, minicomputer systems, mainframecomputer systems, and distributed cloud computing environments thatinclude any of the above systems or devices, and the like.

Computer system/server 12 may be described in the general context ofcomputer system-executable instructions, such as program modules, beingexecuted by a computer system. Generally, program modules may includeroutines, programs, objects, components, logic, data structures, and soon that perform particular tasks or implement particular abstract datatypes. Computer system/server 12 may be practiced in distributed cloudcomputing environments where tasks are performed by remote processingdevices that are linked through a communications network. In adistributed cloud computing environment, program modules may be locatedin both local and remote computer system storage media including memorystorage devices.

As shown in FIG. 4, computer system/server 12 in cloud computing node 10is shown in the form of a general-purpose computing device. Thecomponents of computer system/server 12 may include, but are not limitedto, one or more processors or processing units 16, a system memory 28,and a bus 18 that couples various system components including systemmemory 28 to processor 16.

Bus 18 represents one or more of any of several types of bus structures,including a memory bus or memory controller, a peripheral bus, anaccelerated graphics port, and a processor or local bus using any of avariety of bus architectures. By way of example, and not limitation,such architectures include Industry Standard Architecture (ISA) bus,Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA) bus, VideoElectronics Standards Association (VESA) local bus, and PeripheralComponent Interconnects (PCI) bus.

Computer system/server 12 typically includes a variety of computersystem readable media. Such media may be any available media that isaccessible by computer system/server 12, and it includes both volatileand non-volatile media, removable and non-removable media.

System memory 28 can include computer system readable media in the formof volatile memory, such as random access memory (RAM) 30 and/or cachememory 32. Computer system/server 12 may further include otherremovable/non-removable, volatile/non-volatile computer system storagemedia. By way of example only, storage system 34 can be provided forreading from and writing to a non-removable, non-volatile magnetic media(not shown and typically called a “hard drive”). Although not shown, amagnetic disk drive for reading from and writing to a removable,non-volatile magnetic disk (e.g., a “floppy disk”), and an optical diskdrive for reading from or writing to a removable, non-volatile opticaldisk such as a CD-ROM, DVD-ROM or other optical media can be provided.In such instances, each can be connected to bus 18 by one or more datamedia interfaces. As will be further depicted and described below,memory 28 may include at least one program product having a set (e.g.,at least one) of program modules that are configured to carry out thefunctions of embodiments of the invention.

Program/utility 40, having a set (at least one) of program modules 42,may be stored in memory 28 by way of example, and not limitation, aswell as an operating system, one or more application programs, otherprogram modules, and program data. Each of the operating system, one ormore application programs, other program modules, and program data orsome combination thereof, may include an implementation of a networkingenvironment. Program modules 42 generally carry out the functions and/ormethodologies of embodiments of the invention as described herein.

Computer system/server 12 may also communicate with one or more externaldevices 14 such as a keyboard, a pointing device, a display 24, etc.;one or more devices that enable a user to interact with computersystem/server 12; and/or any devices (e.g., network card, modem, etc.)that enable computer system/server 12 to communicate with one or moreother computing devices. Such communication can occur via Input/Output(I/O) interfaces 22. Still yet, computer system/server 12 cancommunicate with one or more networks such as a local area network(LAN), a general wide area network (WAN), and/or a public network (e.g.,the Internet) via network adapter 20. As depicted, network adapter 20communicates with the other components of computer system/server 12 viabus 18. It should be understood that although not shown, other hardwareand/or software components could be used in conjunction with computersystem/server 12. Examples, include, but are not limited to: microcode,device drivers, redundant processing units, external disk drive arrays,RAID systems, tape drives, and data archival storage systems, etc.

Referring now to FIG. 5, illustrative cloud computing environment 50 isdepicted. As shown, cloud computing environment 50 comprises one or morecloud computing nodes 10 with which local computing devices used bycloud consumers, such as, for example, personal digital assistant (PDA)or cellular telephone 54A, desktop computer 54B, laptop computer 54C,and/or automobile computer system 54N may communicate. Nodes 10 maycommunicate with one another. They may be grouped (not shown) physicallyor virtually, in one or more networks, such as Private, Community,Public, or Hybrid clouds as described hereinabove, or a combinationthereof. This allows cloud computing environment 50 to offerinfrastructure, platforms and/or software as services for which a cloudconsumer does not need to maintain resources on a local computingdevice. It is understood that the types of computing devices 54A-N shownin FIG. 5 are intended to be illustrative only and that computing nodes10 and cloud computing environment 50 can communicate with any type ofcomputerized device over any type of network and/or network addressableconnection (e.g., using a web browser).

Referring now to FIG. 6, a set of functional abstraction layers providedby cloud computing environment 50 (FIG. 5) is shown. It should beunderstood in advance that the components, layers, and functions shownin FIG. 6 are intended to be illustrative only and embodiments of theinvention are not limited thereto. As depicted, the following layers andcorresponding functions are provided:

Hardware and software layer 60 includes hardware and softwarecomponents. Examples of hardware components include: mainframes 61; RISC(Reduced Instruction Set Computer) architecture based servers 62;servers 63; blade servers 64; storage devices 65; and networks andnetworking components 66. In some embodiments, software componentsinclude network application server software 67 and database software 68.

Virtualization layer 70 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers71; virtual storage 72; virtual networks 73, including virtual privatenetworks; virtual applications and operating systems 74; and virtualclients 75.

In one example, management layer 80 may provide the functions describedbelow. Resource provisioning 81 provides dynamic procurement ofcomputing resources and other resources that are utilized to performtasks within the cloud computing environment. Metering and Pricing 82provide cost tracking as resources are utilized within the cloudcomputing environment, and billing or invoicing for consumption of theseresources. In one example, these resources may comprise applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal 83 provides access to the cloud computing environment forconsumers and system administrators. Service level management 84provides cloud computing resource allocation and management such thatrequired service levels are met. Service Level Agreement (SLA) planningand fulfillment 85 provide pre-arrangement for, and procurement of,cloud computing resources for which a future requirement is anticipatedin accordance with an SLA.

Workloads layer 90 provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include: mapping andnavigation 91; software development and lifecycle management 92; virtualclassroom education delivery 93; data analytics processing 94;transaction processing 95; and question answering 96.

The foregoing description of various embodiments of the presentinvention has been presented for purposes of illustration anddescription. It is not intended to be exhaustive nor to limit theinvention to the precise form disclosed. Many modifications andvariations are possible. Such modification and variations that may beapparent to a person skilled in the art of the invention are intended tobe included within the scope of the invention as defined by theaccompanying claims.

What is claimed is:
 1. A computer-implemented method for providinganswers to questions, the method comprising: receiving, by a computer, aquery; identifying, by the computer, a lexical answer type (LAT), arankable criterion, and a probabilistic criterion in the query, whereina rankable criterion refers to components of the query that containordinals and/or superlatives, and a probabilistic criterion refers tocomponents of the query that contain phrases that modify the LAT;generating, by the computer, a set of candidate answers to the querythat are instances of the LAT identified in a structured data source;assigning, by the computer, a rank to each candidate answer, based onthe rankable criterion; assigning, by the computer, a likelihood to eachcandidate answer, that the candidate answer satisfies the probabilisticcriterion, based on a statistic of occurrences of terms related to thecandidate answer and to the probabilistic criterion in text passagesfrom an unstructured data source; selecting, by the computer, one ormore candidate answers, based on the product of the likelihood that thecandidate answer satisfies the probabilistic criterion and thelikelihoods that each candidate answer of lower rank does not satisfythe probabilistic criterion; and transmitting, by the computer, theselected candidate answers.
 2. A method in accordance to claim 1,wherein identifying, by the computer, further comprises identifying aBoolean criterion in the query, wherein a Boolean criterion refers to acomponent of the query that modifies the LAT and is either entirely trueor entirely false for the LAT, and the probabilistic criterion refers tocomponents of the query that contain phrases that modify the LAT and arenot Boolean criterion; and wherein the method further comprises:reducing, by the computer, the set of candidate answers by applying theBoolean criterion.
 3. A method in accordance to claim 1, whereinidentifying, by the computer, further comprises using a natural languageparser or a named entity recognition program.
 4. A method in accordanceto claim 1, further comprising: identifying a subset of the rankedcandidate answers based on a threshold value or filter; and whereinassigning a likelihood further comprises: assigning, by the computer, toeach ranked candidate answer in the identified subset of rankedcandidate answers a likelihood that the ranked candidate answersatisfies the probabilistic criterion, based on a statistic ofoccurrences of terms related to the ranked candidate answer and to theprobabilistic criterion in text passages from an unstructured datasource; and wherein selecting further comprises: selecting, by thecomputer, one or more candidate answers in the identified subset ofranked candidate answers, based on the rank and the likelihood of thecandidate answers.